Communications of the ACM
Estimating the Pace of Change
The scientists generated new artificial data from a computer model that closely matched the empirical data.
Credit: ALol88/Wikimedia Commons
An international research team has developed a new method for determining the pace of change, and has applied it to neural processes.
The method eliminates previous systemic errors in estimating timescales, which relied on empirical data.
Said Roxana Zeraati of Germany's University of Tübingen and the Max Planck Institute for Biological Cybernetics, "The problem is that the empirical data is always measured over finite, often short, time. Because of this, the average dependency between what happens at different points in time is systematically underestimated."
The new method involves the use of a computer model to generate artificial data that closely match the empirical data.
The researchers applied the method to data from neural recordings of the visual cortex.
Said Tübingen's Anna Levina, "We saw that the process in the brain involved two different intrinsic timescales—something that never before has been reported."
From Max Planck Gesellschaft (Germany)
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